98 lines
3.3 KiB
PHP
98 lines
3.3 KiB
PHP
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<?php
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/*
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* Copyright 2014 Google Inc.
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*
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* Licensed under the Apache License, Version 2.0 (the "License"); you may not
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* use this file except in compliance with the License. You may obtain a copy of
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* the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations under
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* the License.
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*/
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namespace Google\Service\Aiplatform;
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class GoogleCloudAiplatformV1IntegratedGradientsAttribution extends \Google\Model
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{
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protected $blurBaselineConfigType = GoogleCloudAiplatformV1BlurBaselineConfig::class;
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protected $blurBaselineConfigDataType = '';
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protected $smoothGradConfigType = GoogleCloudAiplatformV1SmoothGradConfig::class;
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protected $smoothGradConfigDataType = '';
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/**
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* Required. The number of steps for approximating the path integral. A good
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* value to start is 50 and gradually increase until the sum to diff property
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* is within the desired error range. Valid range of its value is [1, 100],
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* inclusively.
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*
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* @var int
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*/
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public $stepCount;
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/**
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* Config for IG with blur baseline. When enabled, a linear path from the
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* maximally blurred image to the input image is created. Using a blurred
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* baseline instead of zero (black image) is motivated by the BlurIG approach
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* explained here: https://arxiv.org/abs/2004.03383
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*
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* @param GoogleCloudAiplatformV1BlurBaselineConfig $blurBaselineConfig
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*/
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public function setBlurBaselineConfig(GoogleCloudAiplatformV1BlurBaselineConfig $blurBaselineConfig)
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{
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$this->blurBaselineConfig = $blurBaselineConfig;
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}
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/**
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* @return GoogleCloudAiplatformV1BlurBaselineConfig
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*/
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public function getBlurBaselineConfig()
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{
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return $this->blurBaselineConfig;
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}
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/**
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* Config for SmoothGrad approximation of gradients. When enabled, the
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* gradients are approximated by averaging the gradients from noisy samples in
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* the vicinity of the inputs. Adding noise can help improve the computed
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* gradients. Refer to this paper for more details:
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* https://arxiv.org/pdf/1706.03825.pdf
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*
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* @param GoogleCloudAiplatformV1SmoothGradConfig $smoothGradConfig
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*/
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public function setSmoothGradConfig(GoogleCloudAiplatformV1SmoothGradConfig $smoothGradConfig)
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{
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$this->smoothGradConfig = $smoothGradConfig;
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}
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/**
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* @return GoogleCloudAiplatformV1SmoothGradConfig
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*/
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public function getSmoothGradConfig()
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{
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return $this->smoothGradConfig;
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}
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/**
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* Required. The number of steps for approximating the path integral. A good
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* value to start is 50 and gradually increase until the sum to diff property
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* is within the desired error range. Valid range of its value is [1, 100],
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* inclusively.
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*
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* @param int $stepCount
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*/
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public function setStepCount($stepCount)
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{
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$this->stepCount = $stepCount;
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}
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/**
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* @return int
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*/
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public function getStepCount()
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{
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return $this->stepCount;
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}
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}
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// Adding a class alias for backwards compatibility with the previous class name.
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class_alias(GoogleCloudAiplatformV1IntegratedGradientsAttribution::class, 'Google_Service_Aiplatform_GoogleCloudAiplatformV1IntegratedGradientsAttribution');
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